HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots

TL;DR

This paper introduces HIPer, a human-inspired scene perception model for mobile robots that integrates recognition, knowledge representation, and interpretation to enhance multifunctional task performance in open-world settings.

Contribution

It presents a novel holistic perception framework based on neuroscience principles, combining image-based detection, hierarchical knowledge bases, and scene analysis for improved robot decision-making.

Findings

Component ablation shows significant performance impact.

Model performs well in simulated and real environments.

Enhances robot scene understanding for complex tasks.

Abstract

Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This paper presents a human-inspired scene perception model to minimize the gap between human and robotic capabilities. The approach takes over fundamental neuroscience concepts, such as a triplet perception split into recognition, knowledge representation, and knowledge interpretation. A recognition system splits the background and foreground to integrate exchangeable image-based object detectors and SLAM, a multi-layer knowledge base represents scene information in a hierarchical structure and offers interfaces for high-level control, and knowledge interpretation methods deploy spatio-temporal scene analysis and perceptual learning for self-adjustment. A single-setting ablation study is used to evaluate the impact of each component on the overall performance for a fetch-and-carry scenario in two simulated and one real-world environment.